Biodegradable Polymer-Based Drug Carriers

Abstract

Biodegradable polymer-based drug carriers have become an essential component of precision medicine because of their ability to provide sustained drug release while minimizing systemic toxicity. Despite substantial advances in polymer synthesis, formulation development remains highly dependent on iterative laboratory experimentation, leading to increased development time and cost. The present study identifies a critical research gap: the limited integration of artificial intelligence with biodegradable polymer selection to simultaneously optimize drug encapsulation, degradation behavior, and release kinetics. To address this challenge, an AI-assisted predictive framework is proposed that combines physicochemical descriptors of polymers and drugs with machine learning models for formulation optimization. Unlike conventional formulation strategies, the proposed framework prioritizes explainable predictions that assist researchers during early-stage formulation design. Recent developments in biodegradable polymers published between 2021 and 2026 are reviewed to establish the scientific foundation of the framework. The study highlights how intelligent computational approaches can reduce experimental burden while improving reproducibility. The proposed concept provides an interdisciplinary bridge between pharmaceutical sciences, artificial intelligence, and digital health technologies